Provides a kernel based locally weighted distance function.
It is defined as follows:
result = max{distP(P,Q), distQ(Q,P)}, where
distP(P,Q) computes the quadratic form distance on the weak eigenvectors of the kernel matrix of P
between two vectors P and Q in feature space.
Computation of the distance component of the weak eigenvectors is done indirectly by computing the difference
between the complete kernel distance and the distance component of the strong eigenvectors:
distP(P,Q) = distP_weak(P,Q) = sqrt(distP_complete(P,Q)^2 - distP_strong(P,Q)^2)
KP_complete(P,Q) is the kernel derived distance between P and Q.
The distance component of the strong eigenvectors KP_strong(P,Q) is computed as follows:
First, the vectors P and Q are projected onto the strong eigenvectors of the kernel matrix of P, which results in
the two vectors Pp and Qp. Then, the euclidean distance is used to compute the distance
between Pp and Qp.
In case of the linear kernel function, this distance is identical to those computed by the LocallyWeightedDistanceFunction with
parameters big = 1.0 and small = 0.0